Introducing Depth Estimation with NVIDIA’s FoundationStereo—a new open tutorial... and codebase for metric depth sensing using Project Aria Gen 2 devices. ⚡️ Real-time ready (20 FPS) 🤖 Zero-shot generalization ⚡️Stereo image rectification and disparity estimation 🔓 Open Source Check out the code and tutorial: 🔌Chris Sweeneyshow more

Want to use tactile sensing but not familiar with... hardware? No worries! Just follow the steps, and you’ll have a high-resolution tactile sensor ready in 30 mins! It’s as simple as making a sandwich! 🥪 🎥 YouTube Tutorial: 🛠️ Open Source & Hardware Guide: 🌐 Project Website: Let’s make robotics more tactile! 🤖 #Robotics #TactileSensing #AI #opensourceshow more

Train a TensorFlow object detection model – then deploy... it on a robot 🤖 Iulia Feroli (Iulia Feroli) shows how to turn a notebook into a real-time object detection app. This tutorial works for any project – though we demonstrate the deployment on (and assisted by!) the #ReachyMini, an open-source robot from Pollen Robotics. Built with PyCharm + Claude Code. 👉 Watch it in action:show more

Depth Any Video with Scalable Synthetic Data AI physicists... and chemists continue to make strides in depth estimation from video. Check out this new paper featuring some impressive examples. See the thread for more details (unfortunately no code yet). Abstract: Video depth estimation has long been hindered by the scarcity of consistent and scalable ground truth data, leading to inconsistent and unreliable results. In this paper, we introduce Depth Any Video, a model that tackles the challenge through two key innovations. First, we develop a scalable synthetic data pipeline, capturing real-time video depth data from diverse game environments, yielding 40,000 video clips of 5-second duration, each with precise depth annotations. Second, we leverage the powerful priors of generative video diffusion models to handle real-world videos effectively, integrating advanced techniques such as rotary position encoding and flow matching to further enhance flexibility and efficiency. Unlike previous models, which are limited to fixed-length video sequences, our approach introduces a novel mixed-duration training strategy that handles videos of varying lengths and performs robustly across different frame rates 0 - even on single frames. At inference, we propose a depth interpolation method that enables our model to infer high-resolution video depth across sequences of up to 150 frames. Our model outperforms all previous generative depth models in terms of spatial accuracy and temporal consistency.show more

MARVEL JUST PLUCKED $5,000,000 OUT OF THEIR VFX BUDGET... TO BUY OUT A 20-YEAR-OLD WHO REBUILT SPIDER-MAN IN 120 HOURS Disney spent $250,000,000 and forced 400 animators into crunch to render a masked, miserable hero. A broke student booted a single neural engine and built a maskless, overjoyed superhero in 5 days with zero render software. Marvel panicked, realized his pipeline beat their internal renders, and dropped a $5M buyout. Here is the exact technical stack that forced a $150B studio to write the check: > ZERO MOCAP HARDWARE - Driven by open-source spatial pose estimators. No physical tracking suits or clean plates required. > MID-AIR FACIAL LOCK - Locked consistent face features across high-speed lighting using a hyper-focused ControlNet depth harness. > LIQUIDATED CGI PIPELINES - Replaced 3D environments, manual keyframes, and render farms with real-time latent frame interpolation.show more

BaBot: The Ball-Balancing Robot - Real-time PID control on... a 2-axis platform, powered by a microcontroller using the same chip as an Arduino. - Precision servos and IR sensors track the ball with speed and accuracy. - Perfect for learning control systems, teaching robotics, or showing off your engineering skills. - Fully open-source & ready to build at Video Credit: Johan Link #engineering #technology #robots #roboticsshow more

(1/n) 🚀 With FastVideo, you can now generate a... 5-second video in 5 seconds on a single H200 GPU! Introducing FastWan series, a family of fast video generation models trained via a new recipe we term as “sparse distillation”, to speed up video denoising time by 70X! 🖥️ Live demo: (Thanks to @gmicloud for the support!) 🔗 Blog: 🔓 We fully open-source our models, code, and data with Apache-2.0 licensesshow more

Robot Utility Models (RUMs) enable basic tasks – door... opening, drawer opening, object reorientation, etc. – at ~90% accuracy without ANY finetuning (i.e. zero-shot) in unseen new environments. Fully open source!!! models, data, code & hw. We think this is super exciting, why?👇 1. Unlocks many practical home utility tasks that often involve these basic tasks as part of an action chain. “Go get me a fork” involves opening the kitchen door and then opening the cutlery drawer. 2. This works well **zero-shot in unseen and new** environments, which is practically a huge deal. Turn the robot on, and get going. 3. The recipe for building a new model is fairly generic, and we think with a bit more refinement this can be a general recipe to build many more Utility models. More details and access 👇show more

🚀 Introducing EgoExo Forge - built on top of... Rerun, Gradio, and Hugging Face hub (I’ll be in San Francisco July 21–29 — if you’re into robotics, egocentric AI, large-scale data collection, or just want to chat, DM me!) In my opinion, large-scale, diverse, and high-quality data is still the largest bottleneck for generalized robotics deployment. I believe that some version of imitation learning from human examples will be the most scalable + clean way to train humanoid robots 🤖 (similar to what Tesla did for Full Self Driving). Teleop is too expensive to collect a large enough dataset in a reasonable manner, so passive collection via egocentric (and in certain cases, exocentric) views feels like the right bet. Over the past few months, I've been trying to build out the scaffolding for this and using Rerun as my underlying infrastructure. Data being collected needs to be easily inspectable + time series and rerun provides the right tooling for this. My goal is to first build out a ground truth representative dataset from already existing open source data, generate some reasonable baselines, and then go out and collect my own data that adheres to the defined schema. 🔍 Starting with open-source datasets 1. EgoDex from Apple 2. HOCap from Nvidia and the University of Texas at Dallas 3. Assembly101 from Meta All these different datasets have different sensor configurations + annotations, so my goal with egoexo-forge is to have one consistent labeling scheme + data layout. I built a data pipeline that aligns all of the different datasets in one general schema assuming the COCO133 keypoint layout that allows for exo+ego, ego only, or exo only Since the scaffolding is already there, it becomes MUCH easier to add other datasets. So the next ones that I'll be including are HD-EPIC kitchens dataset, HOT3D, and finally my own personal iPhone + insta360 go collection method. Once I have a diverse variety of datasets, I'll double down on what I believe to be the key algorithms required to make useful data for imitation learning 📊 1. Camera Pose estimation via SLAM/SFM for ego perspective (and automatic calibration for exo) 2. Human pose estimation for both egocentric + exocentric views 3. Metric 3D reconstruction + object tracking I'll be setting up reasonable open-source baselines for each of these to validate that these datasets work, and then finally try to use the generated datasets for some imitation learning via the pi0-lerobot repo I've been working on. I plan on making a blog post + providing more info on all of this in the near future so stay tunedshow more

Let's do ASMR of instant Ramen 🍜 GPT Image... 2 and Seedance 2.0 on Higgsfield AI 🧩 Prompt : Create a premium 16:9 storyboard infographic for an Instant Ramen ASMR commercial using the uploaded image as the exact character reference, maintaining perfect facial consistency throughout. Design a clean white creative agency layout with the title "STORYBOARD – INSTANT RAMEN – ASMR NOODLE EXPERIENCE" and top info cards for Duration, Style, Audience, and Audio. Arrange 6 rounded storyboard panels with numbered badges. Show: (1) girl smiling while presenting the ramen cup, (2) opening the lid and adding seasoning, (3) pouring hot water with rising steam, (4) stirring noodles with chopsticks, (5) lifting and slurping noodles, (6) final hero shot with ramen and a thumbs-up. Add playful white doodles like HELLO!, OPEN!, SPRINKLE!, POUR~, STIR!, SLURP!, YUM!, PERFECT! Include VISUAL, ACTION, and DIALOGUE sections below each panel and an Audio Experience (ASMR Only) box at the bottom. Use cozy bedroom lighting, warm tones, luxury commercial styling, cinematic macro food photography, photorealistic steam, shallow depth of field, elegant brown accents, polished client-pitch presentation, 16:9 widescreen, and no timestamps. #HiggsfieldCPPshow more

A 19-year-old student from China, Zhang Wei, developed an... AI radar and sold it to Hong Kong for $550,000 He created it using Claude, spending just $20 and a month on development He walked into the Hong Kong administration office with a flash drive and asked for just 5 minutes of their time. 30 minutes later, he walked out with a check for $550,000 The code, connected to a camera, detects speed in real time. If the speed exceeds the limit, Claude takes a video clip and identifies the owner by the car's license plate. The video and the fine are then automatically sent to the owner's email address Unlike a conventional radar that only takes a photo and doesn't always work, this AI radar eliminates disputes because it captures video and makes the process fully autonomous by sending out the fines on its own The article includes the ready-to-use configurations.show more

Wow! Send A File By Just The Camera! A... vibe coder used AI to build a file transfer system that sends data between two phones using only a screen and a camera. One phone displays a new type of animated QR codes while the other scans them to rebuild the file, with no Wi-Fi, Bluetooth, or cables needed. It is fully optical and local. The system uses fountain codes that create each QR frame as a random mix of file data. This keeps transfers working even if some frames are missed, reaching speeds of about 129 KB/s for a 2 MB image. The entire project was built in one night and released as open source. The idea came from a music project where the developer wanted to share MP3 files without streaming or using the same network. Animated QR codes became the solution, showing a creative new way to transfer files with everyday phone hardware. GitHub link:show more

Introducing a new tool called "SideChannel". A secure alternative... to OpenClaw. Utilizes signal for communication and has Claude integration. I built SideChannel, an open-source Signal bot that connects Claude AI to your entire development workflow. End-to-end encrypted. From your pocket. The real power is autonomous development. Send one message like "Build a REST API with auth, pagination, and tests" and SideChannel will: - Generate a full PRD with stories and atomic tasks. - Dispatch up to 10 parallel workers (each running Claude). - Independently verify every task with a separate Claude context. - Run quality gates to catch regressions - Auto-fix failures. - Send you progress updates via Signal as work completes. Every piece of code is reviewed by a separate AI context using a fail-closed security model. If it detects security issues, backdoors, or logic errors — the code gets rejected automatically. No rubber stamps. It also has memory that actually works. Conversations are stored with vector embeddings for semantic search. Claude remembers your project conventions, past decisions, and what's been tried before. It gets smarter about your codebase over time. Other things I'm proud of: - Plugin framework for extending with custom commands. - Multi-project support with per-user scoping. - Rate limiting, path validation, phone allowlist. - Git checkpoints before every task, atomic commits after. - Stale task recovery, circular dependency detection. - Works on Linux and macOS, one-command install. It also integrates into OpenAI or Grok (optional) for more Generative AI response for simple things like "Whats the weather in New York City right now?".show more

You can't 3D reconstruct glass from images... ...WRONG! Thanks... for video diffusion, now just about anything is possible! Introducing...Diffusion Knows Transparency (DKT) Transparent and reflective objects usually break robot vision and photogrammetry pipelines because they don't follow the "solid object" rules standard cameras expect. DKT is a new AI model that repurposes the "internal physics engine" found in video generation models to solve this problem. Researchers took a massive video diffusion model (WAN) and fine-tuned it using a custom-built synthetic dataset to turn it into a high-precision depth sensor. To train the AI, they built the first massive synthetic video library of transparent objects, 1.32 million frames of perfectly labeled glass and metal objects in motion. Without ever seeing a "real" labeled video of glass during training, the model (DKT) outperformed all previous specialized systems on real-world benchmarks (ClearPose, DREDS). They created a "lightweight" 1.3B parameter version that runs fast enough (0.17s per frame) to be used on actual robot hardware. Two reasons I find this project important: 1. It further proves that synthetic data will be essential for training the next generation vision models. 2. In real-world robotic tests, using DKT's depth maps nearly doubled the success rate of robot arms trying to pick up objects on tricky reflective or translucent surfaces. At home robots will need to interact with these types of objects on a daily basis. Check out the project page here: Code is LIVE! #Computervision #Robotics #AIshow more

🔥 VIDU Multi-Entity Consistency Give Vidu 2/3 images and... it’ll turn them into a video—it’s pure magic! ✨ Your own characters interacting with objects and in the exact environment you want! Ads, movies… endless possibilities, and this is just the beginning! Thanks @Viduforhuman The future is a carrot! 🥕 Plus, how about grabbing any frame from a Vidu-generated video "from scratch" and throwing it into another AI video or image tool to push your project even further? For now, check out the comment below: I scaled up a frame with Magnific.ai and fed it into Runway to create a dynamic shot using full camera control. But fingers crossed I can soon use #ReCapture by Bisho & team to generate new shots from the same video!show more

NanoBanana 2 just made your static ad agency obsolete.... And I just open sourced the entire tool. Drop your product page URL. It pulls your logos, product images, fonts, colors, and brand voice automatically. Builds a full brand guide for you. Then generates ad creatives at scale using nearly 4,000 high-performing ad templates across 8 niches. It dynamically matches the best templates to your brand and brief. Here's what makes it different: → Instant resizing Get any ad in 1x1, 4x5, 9x16 with one click. No regeneration. No broken text. → Highlight-to-edit See an issue? Highlight the area and tell it what to fix. → Multiple brand profiles Run different brands or segments from one tool. → Auto persona building from real customer reviews → Multiple QC loops on briefs and final assets Catches AI-isms before you do. → Upload your own templates or use ours Runs locally. Just needs your Claude and Google API keys. This is the lite version of what we use internally. You get the full finished tool AND the open source code to make it your own. Creatives still design the system, this handles iteration and scale. Want a copy to download? 1. Like this post 2. Comment "AI" Will DM you the tool along with a tutorial shortly after.show more

Seedance 2.0 + Claude Code is f*cking insane 🤯... I built a Claude skill that creates UGC ads on demand. One product + one prompt = the AI creator, the script, the scene-by-scene shot list, and the finished video. All inside Claude Code. Perfect for DTC brands and agencies who can't afford to keep paying $500-$1,500 per UGC video and waiting 2 weeks for revisions. This skill eliminates the entire loop: → Tell Claude the product, ad angle, and length → Skill writes the GPT Image 2.0 prompt to generate the AI creator from scratch → Skill writes every scene prompt, dialogue line, and delivery direction → Pipes it into Seedance 2.0 with character + product + voice locked → Speed up + caption in CapCut → Ship the ad in 20 minutes No more paying $11 per video on Arcads. No more 2-week revision cycles. No more PR boxes to creators who ghost you. What you get: → Perfect character consistency across every scene → Voice consistency that holds clip-to-clip → Real product fidelity using your actual product photo as a reference → Multi-scene day-in-life, testimonial, and action-shot formats out of the box Built 100% with a Claude skill + Seedance 2.0. I recorded a full step-by-step tutorial showing the exact workflow so you can build these AI UGC ads yourself. Want the full breakdown? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)show more

Introducing MiniMax H3 on Argil Some days, the future... decides to accelerate. Two days ago, we launched the new version of Argil. Today, I’m extremely happy to announce our partnership with MiniMax (official): H3 is live in Argil. We tested it internally. The quality is really solid. An intelligence that reads text, images, video and audio as one language. That captures creative intent at a glance and already delivers production-ready content. MiniMax H3 will soon be open source. And that’s a real game changer. For the occasion, Golden Ticket: 40% off Pro & Business yearly during MiniMax Hive until tonight. Comment "argil" I’ll send you the code in DM. To those who create: the field just got wider. Link 👇show more

$KNDX 🤖 Theres 3 big narratives that are sending... coins left right and centre rn. 🚀 #AI, #Gamefi, & #NFTs 🔹Theres 50% mindshare for #AI. 🤖 🔹#GameFi mcap is hitting ATH's with #OfftheGrid, $XBG and $SUPER making spectacular moves. 🎮 🔹NFTs and the #Metaverse are making a strong comeback with $APE up 100% over the weekend. 🐵 What if there's a project that touches all these trending narratives with groundbreaking technology to disrupt all 3 of them? 🔥 💡- That's where $KNDX comes in. -💡 Kondux is a cutting-edge Web3 SaaS platform, combining NVIDIA’s Omniverse, AI, Blockchain, and dynamic NFTs to revolutionize secure asset management across industries. 👏 Their flagship product, kNFTs, are 3D digital assets usable across Metaverse and Gaming platforms, AR/VR/XR environments, and manufacturing applications. Kondux’s scalable model opens new revenue streams by enabling effective digital asset monetization. 💰 Kondux is the first Web3 project to integrate VFX pipelines with NVIDIA’s Omniverse and bringing it onto the Blockchain. ⛓️ It is also the only Web3 project with a *Select Status Partnership* with NVIDIA, operating under NVIDIA NDAs and working with them directly for more than 2 years. About their NVIDIA Integrations: 🤖 🔹There are three areas of the Kondux tech stack that coincide with three divisions of NVIDIA: 📡GDN (Graphics Delivery Network, the backbone of GeForce Now) 💡Omniverse for 3D aspects such as, geospatial data, real world physics, lighting, and raytracing 🤖NVIDIA AI Foundation, which covers many aspects of #AI, including inference and deployment scaling. The convergence of all these components lie within .USD file format . 🔹 They are the first blockchain project to integrate NVIDIA’s Omniverse Cloud and Graphics Delivery Network (GDN) to provide high-quality 3D content accessible on any device without requiring high-end hardware. 🔹 This setup streamlines content management, democratises access to resource-intensive 3D content, and enables real-time interaction with 3D NFTs. Now, I haven’t seen any crypto project so deeply connected with NVIDIA and NVIDIA technology. GDN is a HUGE competitive advantage. With it, the need for #GPU’s basically goes out the window. 🤯 Now lets take a look at some of the other main features... 👀 OpenUSD (Universal Scene Description): 📽️ 🔹 Kondux is leveraging USD technology, developed by Pixar and used by Meta, Apple, Microsoft and other industry leaders to enhance 3D graphics and interoperability within its creative ecosystem. 🔹 Originally created for high-end film production, USD now supports a variety of applications, including gaming and virtual reality, making it a key asset for Kondux. kNFT's: 🎨 🔹 Kondux is pioneering a new category of NFTs known as kNFTs, which aim to redefine NFT utility through innovative features. 🔹 A standout feature is the upgradeable aspect provided by Kondux DNA, allowing kNFTs to transform and combine with other NFTs, creating limitless possibilities in art, gaming, and music. 🔹Through the Kondux AI portal it will be possible to communicate with kNFTs. They can learn and adapt. This AI technology is revolutionary because it makes human to kNFT interaction possible, turning it into a unique, personalized experience. Check out the clip of kNFTs in Unreal Engine 5 gameplay below. 👇 Kondux is a very obvious utility play with huge upside because it’s multi narrative. 📈 It's seriously groundbreaking stuff that they’re about to launch. 🚀 After speaking with the team there’s no doubt in my mind this will do crazy big numbers in the next months. 🤑show more

Release: LichtFeld Studio v0.5.3 is out! With 316 commits... merged into master, this release is a huge step forward for LichtFeld Studio. What's new in v0.5.3 • Vulkan viewer/rendering migration: New Vulkan viewport pipeline, pass graph, VkSplat renderer, Vulkan point-cloud renderer, 3DGUT/VkSplat support, improved alpha/depth composition, tighter CUDA/Vulkan interoperability, and device matching on multi-GPU systems. • RAD + LOD workflow: Added RAD file export/import, RAD LOD viewer, Spark-style GPU LOD selection, GPU-driven page prefetching, a bounded VRAM pool, out-of-core PLY-to-RAD LOD conversion, and RAD import/export speedups of approximately 3–5×. • HiGS / macro-tile inference: Added a macro-tile inference path for the Vulkan viewer, including macro sorting, batched rasterization, composition, and capacity management. • Asset Manager: Added and significantly enhanced the Asset Manager with thumbnails, SH information, faster synchronization, import-from-URL support, docked mode, data-loading popup integration, and general UI cleanup. • Viewport export: Integrated viewport export directly into the application as a toolbar/overlay tool, added fast render_view_u8-style readback paths, fixed high-resolution clipping issues, improved orthographic export parity, resolved 32K image/video export problems, and added post-export GPU resource cleanup. • Selection and tooling: Added and reworked selection toolbar controls, the Select menu, ring selection, color eyedropper, distance-from-center selection, faster point-cloud and zoomed-out selection paths, Vulkan measurement tool fixes, and drag-and-drop scene graph improvements. • UI/RmlUi platform work: Major RmlUi redesign efforts, hot reloading for RML/RCSS/Python UI files, reactive UI/store integration, viewport toolbar flyouts, improved histogram interactions, input settings enhancements, custom TRS gizmos, and numerous panel, tooltip, and localization fixes. • Windowing and UX: Added borderless window support, title bar drag/maximize/restore behavior, work-area-aware maximize functionality, resize responsiveness and performance improvements, and DPI/UI scaling fixes. • Training and data features: Added adaptive depth loss and depth gradients for the EWA rasterizer, mask loading/application fixes, a new combined Ignore+Segment mask mode, --add-splat, --freeze, improved checkpoint and training state handling, and training speed and VRAM optimizations. • COLMAP/equirectangular support: Added SPHERICAL/equirectangular camera model support and canonical EQUIRECTANGULAR handling, along with fixes for undistortion and camera export. This release will be available to all supporters as a Windows binary via approximately in about an hour. At the same time, LichtFeld Studio remains committed to being free and open source under GPLv3 and can also be built directly from source. Please consider supporting the ongoing development of LichtFeld Studio through a donation via the portal or the supporters page. Thank you to everyone who supports this project financially, contributes code, reports bugs, provides datasets, helps with the website, and contributes in countless other ways. A special thank you to our foundational sponsor Core11 and our Gold Sponsor Volinga, whose support has helped make the current state of the software possible. Thank you as well to every donor and to all of our new Bronze Sponsors. Looking ahead to v0.6 For the next major release, work will focus primarily on stability and user experience. This includes improved cleanup workflows and the ability to modify training parameters while training is in progress. I would also like to introduce a native .licht project format that allows users to save and restore their complete editor state. You can find links to our main sponsors below. Please also visit our website to discover all our Bronze Sponsors. Hint: We do not yet have a Silver Sponsor or Platinum 😉show more